Lactate Correlation Paper: Nanostring Analysis of All Data

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Section 1: Expression analysis and model generation using nanostring data.

Fig. 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels
Fig. 2: Heatmap with data scaled by gene/row and clustered by sample/column
Fig. 3: Mean relative expression of the geneset correlated with 3 hour lactate levels
Table 1: Multiple Linear Regression Predictive Model
Fig. 4: Applying the predictive model to the observed expression levels
Fig. 5: Checking model conditions
Fig. 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the model.

Fig. 7: Incorporating the additional samples into the established model
Fig. 8: Correlation between the models predicted lactate levels and donor metrics
Fig. 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels
Fig. 10: Heatmap with data scaled by gene/row and clustered by sample/column
Fig. 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels
Fig. 12: PCA using scaled data for all 17 samples
Fig. 13: PCA using scaled data for the original 10 samples only
Table 2: Multiple Linear Regression Predictive Model generated using all samples
Fig. 14: Applying the predictive model to the observed expression levels of all 17 samples
Fig. 15: Verifying model conditions
Fig. 16: Correlation between the new models predicted lactate levels and donor metrics

Section 1: Expression analysis and model generation using nanostring data.

Expression levels of putative biomarkers identified with RNAseq were validated using a custom nanstring panel and tested for conserved correlation with 3 hour lactate levels. Genes that met the biomarker criteria in both analyses were then used to generate a multiple linear regression predictive model.

Fig. 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels

Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation cutoff.

Fig. 2: Heatmap with data scaled by gene/row and clustered by sample/column

Functional assessment is labeled above the columns

Fig. 3: Mean relative expression of the geneset correlated with 3 hour lactate levels

Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Table 1: Multiple Linear Regression Predictive Model

## 
## Call:
## lm(formula = LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + GSTA1 + 
##     GSTA2, data = mlm.data)
## 
## Residuals:
##        FV2        LV2        LV3        FV3        LV1        FN1        FN2 
## -1.7772858 -0.4726029 -0.6974732 -0.3509557  2.7526045  0.8175029 -0.3654356 
##        LN1        FN3        LN3 
## -0.0006994 -0.0181005  0.1124458 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.1919727  2.8772531  -1.457    0.282
## EPHX1        0.0007600  0.0016188   0.469    0.685
## TKT          0.0245085  0.0283383   0.865    0.478
## GPX2        -0.0013174  0.0018790  -0.701    0.556
## JUN          0.0082264  0.0293291   0.280    0.805
## CYP2B6      -0.0004416  0.0004110  -1.074    0.395
## GSTA1       -0.0001505  0.0001989  -0.757    0.528
## GSTA2        0.0013193  0.0010763   1.226    0.345
## 
## Residual standard error: 2.488 on 2 degrees of freedom
## Multiple R-squared:  0.9761, Adjusted R-squared:  0.8926 
## F-statistic: 11.68 on 7 and 2 DF,  p-value: 0.0811

Fig. 4: Applying the predictive model to the observed expression levels

Fig. 5: Checking model conditions

Fig. 6: Correlation between the models predicted lactate levels and donor metrics

## Warning in cor(temp.meta, temp.predict): the standard deviation is zero

Section 2: Incorporating additional samples into the model.

In order to validate the capabilities of these genes to serve as biomarkers, expression was measured in 7 additional livers with the custom nanostring panel.

Fig. 7: Incorporating the additional samples into the established model

Fig. 8: Correlation between the models predicted lactate levels and donor metrics

Fig. 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels

The black lines represent 80% correlation and the red represents 60% correlation

Fig. 10: Heatmap with data scaled by gene/row and clustered by sample/column

Functional data and DCD/DBD status is labeled above the columns

Fig. 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels

Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Fig. 12: PCA using scaled data for all 17 samples

Fig. 13: PCA using scaled data for the original 10 samples only

Table 2: Multiple Linear Regression Predictive Model generated using all samples

## 
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + 
##     GSTA1 + GSTA2, data = mlm.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4583 -2.4911 -0.7999  1.5243  9.4388 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -1.080e+00  3.165e+00  -0.341   0.7406  
## EPHX1       -1.944e-03  1.240e-03  -1.568   0.1513  
## TKT          3.048e-02  1.193e-02   2.555   0.0309 *
## GPX2        -2.012e-05  1.240e-03  -0.016   0.9874  
## JUN          9.958e-04  3.370e-03   0.295   0.7743  
## CYP2B6       7.556e-05  3.593e-04   0.210   0.8381  
## GSTA1        1.466e-04  2.032e-04   0.721   0.4890  
## GSTA2        1.223e-03  7.761e-04   1.576   0.1496  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.661 on 9 degrees of freedom
## Multiple R-squared:  0.7791, Adjusted R-squared:  0.6073 
## F-statistic: 4.535 on 7 and 9 DF,  p-value: 0.01976

Fig. 14: Applying the predictive model to the observed expression levels of all 17 samples

Fig. 15: Verifying model conditions

Fig. 16: Correlation between the new models predicted lactate levels and donor metrics

Lactate Correlation Paper: Nanostring Analysis of All Data

John Santiago

2022-03-23

Return to Home Page

Table of Contents

Section 1: Expression analysis and model generation using nanostring data.

Fig. 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels

Fig. 2: Heatmap with data scaled by gene/row and clustered by sample/column

Fig. 3: Mean relative expression of the geneset correlated with 3 hour lactate levels

Table 1: Multiple Linear Regression Predictive Model

Fig. 4: Applying the predictive model to the observed expression levels

Fig. 5: Checking model conditions

Fig. 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the model.

Fig. 7: Incorporating the additional samples into the established model

Fig. 8: Correlation between the models predicted lactate levels and donor metrics

Fig. 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels

Fig. 10: Heatmap with data scaled by gene/row and clustered by sample/column

Fig. 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels

Fig. 12: PCA using scaled data for all 17 samples

Fig. 13: PCA using scaled data for the original 10 samples only

Table 2: Multiple Linear Regression Predictive Model generated using all samples

Fig. 14: Applying the predictive model to the observed expression levels of all 17 samples

Fig. 15: Verifying model conditions

Fig. 16: Correlation between the new models predicted lactate levels and donor metrics

Section 1: Expression analysis and model generation using nanostring data.

Fig. 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels

Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation cutoff.

Fig. 2: Heatmap with data scaled by gene/row and clustered by sample/column

Functional assessment is labeled above the columns

Fig. 3: Mean relative expression of the geneset correlated with 3 hour lactate levels

Table 1: Multiple Linear Regression Predictive Model

Fig. 4: Applying the predictive model to the observed expression levels

Fig. 5: Checking model conditions

Fig. 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the model.

In order to validate the capabilities of these genes to serve as biomarkers, expression was measured in 7 additional livers with the custom nanostring panel.

Fig. 7: Incorporating the additional samples into the established model

Fig. 8: Correlation between the models predicted lactate levels and donor metrics

Fig. 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels

The black lines represent 80% correlation and the red represents 60% correlation

Fig. 10: Heatmap with data scaled by gene/row and clustered by sample/column

Functional data and DCD/DBD status is labeled above the columns

Fig. 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels

Fig. 12: PCA using scaled data for all 17 samples

Fig. 13: PCA using scaled data for the original 10 samples only

Table 2: Multiple Linear Regression Predictive Model generated using all samples

Fig. 14: Applying the predictive model to the observed expression levels of all 17 samples

Fig. 15: Verifying model conditions

Fig. 16: Correlation between the new models predicted lactate levels and donor metrics